Sleep Quality Estimation using Accelerometer Data from Thigh-Mounted Devices During in Free Living Conditions

• data preparation, big time-consumer is handling raw acc data
• only thigh data used. HSBC and other is only thigh data…
• all zm recording is considered as in-bed (sensor problem?)
• no sleep stages, only sleep/awake
• sensor problems during sleep, up to 20 consecutive epochs (200 sec) are treated as sleep
• Combine binary classifiers to produce multilabel outcomes (i.e., binary Relevance)
• Evaluated combined classifiers using weighted micro-averaging.
• adjust threshold for in bed awake or give edge in tie breakers??
• zm sleep stats inclusion criteria
• three approaches: tw- binary (risk of ghost classes), binary relevance (choosing the right tie breaker), and multiclass

\[\frac{dx_c}{dt}=\frac{\pi}{12}\begin{cases}\mu(x_c-\frac{4x^3}{3})-x\begin{bmatrix}(\frac{24}{0.99669\tau_x})^2+kB\end{bmatrix}\end{cases}\]

But as demonstrated by Walch et al. (2019), a simple cosine function does the trick just as well :)

• Multiclass classifier with three labels: in-bed-asleep, in-bed-awake, and out-bed-awake
  • pros: simplicity, speed, joint modeling, consistency across classes
  • cons: struggle with class imbalance, performance

• Two binary classifiers: in-bed/out-bed and asleep/awake
  • pros:
  • cons: ghost classes

• Three binary classifiers: in-bed-asleep 1/0, in-bed-awake 1/0, and out-bed-awake 1/0
  • pros: more flexible, can better handle class imbalance
  • cons: complex training, low speed, feature redundancy